{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(5, 2), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=1, shuffle=True, solver='lbfgs', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "#X = [requirement no., level no.]\n",
    "#y = [true/false (1/0)]\n",
    "\n",
    "X = [[0., 1.], [0., 2.], [1., 2.], [5.,1], [3.,0.], [0.,1.]]\n",
    "#first result adds in noise\n",
    "y = [0, 1, 1, 0, 1, 1]\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
    "                     hidden_layer_sizes=(5, 2), random_state=1)\n",
    "\n",
    "clf.fit(X, y)                         \n",
    "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto',\n",
    "              beta_1=0.9, beta_2=0.999, early_stopping=False,\n",
    "              epsilon=1e-08, hidden_layer_sizes=(5, 2),\n",
    "              learning_rate='constant', learning_rate_init=0.001,\n",
    "              max_iter=200, momentum=0.9, n_iter_no_change=10,\n",
    "              nesterovs_momentum=True, power_t=0.5, random_state=1,\n",
    "              shuffle=True, solver='lbfgs', tol=0.0001,\n",
    "              validation_fraction=0.1, verbose=False, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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